Data ecosystem modernization is a huge undertaking that can be fraught with challenges. From data integration to governance, there are many potential pitfalls that can occur during the modernization process.
In this article, we will discuss some of the most common challenges and how to address them. By understanding these challenges and taking steps to mitigate them, you can ensure a successful data ecosystem modernization project.
1. Data Integration
One of the most common challenges during data ecosystem modernization is data integration. When organizations modernize their data ecosystems, they often need to integrate disparate data sources. Unless you’re using highly reliable workflow software, this can be a difficult and time-consuming process, particularly if the data sources are not compatible.
To address this challenge, it is important to have a plan for how you will integrate your data sources. This plan should include what data needs to be integrated and how you will achieve compatibility between your data sources. With an effective data integration plan, you’ll be able to avoid issues down the line.
2. Data Governance
Another common challenge faced during data ecosystem modernization is data governance. When modernizing their data ecosystems, organizations often need to put in place new governance structures. This can be a difficult process, as it can be hard to establish who has access to what data and how this data should be used.
It is important to have a clear plan for your data governance structure. This plan should include who will have access to which data and how this data will be used. By having a clear plan for data governance, you can avoid many of the pitfalls associated with this challenge.
3. Data Security
When undergoing modernization of data ecosystems, it’s common for organizations to put in place new security measures—which might not be an easy process. That’s mainly because it can be hard to establish how to secure your data against potential threats. To address this challenge, make sure to have a clear plan for your data security.
The plan should include what data needs to be secured and how you will secure it. By having a well-thought-out data security plan, you increase the chances of avoiding any pitfalls associated with this challenge.
4. Data Quality
Another challenge during data ecosystem modernization has something to do with data quality. When organizations modernize their data ecosystems, they often need to improve the quality of their data. What makes this tough is the fact that it’s hard to establish how to measure and improve data quality.
Addressing this challenge starts with having a concrete plan for your data quality improvement. It should include what metrics you will use to measure data quality and how you will improve it. By having a clear plan for data quality improvement, you can certainly prevent any associated pitfalls.
By understanding the common challenges mentioned above and taking relevant steps to address them, you can ensure a successful data ecosystem modernization project.
